Sampling procedure.
To optimize for the parameters \(\theta \)  during training we randomly sample a set of points \({\mathbf {p}}\in {\cal P}({\cal V})\)  within the canonical space.
\({\cal P}({\cal V})\)  contains a mixture of uniform sampling and importance sampling around the ground-truth mesh's surface \(\mathcal {V}\)  at time-step \(t_c\) . Similar strategies are also used in prior works [1]}, [2]}.
